CAREER: Network modulation of cortical neuron computation

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The function of sensory neurons is typically defined by the relationship between sensory stimuli and their responses; however, in the cortex of awake animals, sensory responses account for only a fraction of neural activity. While activity not driven by the stimulus is often considered "noise" and neglected in experiments, such ongoing cortical activity has been linked to a number of processes related to cognition, and can be influenced by attention, tasks, and perception itself. It remains unclear how to relate such ongoing cortical activity to the processing of sensory stimuli, and more generally why it appears to play such a prominent role in sensory neuron function. The goal of this project is to establish a new framework for understanding stimulus processing in the context of ongoing cortical activity, and thereby derive a much richer understanding of sensory neuron function. This work will leverage the wealth of information about activity within the cortical network that is now typically available from multi-electrode recordings, using experiments performed by collaborating laboratories in the awake visual cortex using tailored visual stimuli. The first aim is to develop new statistical approaches for identifying relevant modulatory signals detectable from these multi-electrode recordings, and perform detailed characterizations of stimulus processing in the context of these signals. The second aim is to study specific contexts where cortical activity is shaped by known network inputs, such as during saccadic eye movements, in order to directly link the modulation of stimulus processing to larger descriptions of sensory neuron function.This work will provide potentially transformative insights into the relationship between sensory processing and cognitive function. The educational component of this proposal will integrate computational and quantitative approaches into general neuroscience coursework, and involve students at the graduate, undergraduate, and high school levels, in computational analyses of complex neurophysiological data.